As the power distribution system grows and more sensors are added, more data is created every day. This data can be crucial for finding faults, but there is now so much data that it ends up being unused. This presents a valuable opportunity to gain crucial insights into the continuously expanding and increasingly complex power distribution system. This thesis aims to utilize this valuable resource by finding a feature extraction method that can find valuable features in real-world data, use these features to cluster the data, separate different faults into different clusters, and develop a method for how these clusters can be classified, making it possible for an expert to classify large amounts of data quickly. In the end, an autoencoder was used for the feature extraction. The features could be used to cluster both labeled and unlabeled real-world data. The clustering also made it possible to find errors in the labeled data, as the data from one class were clustered into two clusters. A method was developed that allowed the clusters of 32454 unlabeled datapoints to be accurately classified in approximately 30 minutes. This thesis has successfully developed a method that can be used to get insights from large amounts of data, helping experts within the field of power engineering build the power distribution system of the future.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-63409 |
| Date | January 2023 |
| Creators | Bjurhager, Emanuel |
| Publisher | Mälardalens universitet, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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